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How to retrain a DMAD student from the released training code with DMAD

Use the train folder and the published Wan2.1 training data to start a DMAD training run.

  • Content
  • Advanced
  • 4 hours
  • From the README
  • Any agent

Who it is forA researcher or ML engineer who wants to reproduce or extend DMAD training on Wan2.1 data.

The problemReproducing a distillation result needs the training code, the data, and the environment set up together.

The outcomeA training run that starts from the published Wan2.1 data with the code in train/wan.

What you need first

  • A CUDA GPU: 80 GB for the default path, 8 GB and up with --low-vram
  • Python 3.12 with conda and PyTorch for CUDA 12.6
  • About 170 GB of free disk space for the MiniMax-H3 base components
  • A Hugging Face account that has accepted the MiniMax H3 Community License

Steps

  1. Read train/wan/README.md, including its data section, and follow its environment setup.
  2. Download the data with hf download ZhengmingYu/DMAD-Wan-data --repo-type dataset --local-dir data/wan. (inferred, check the README)
  3. Point the training config at the downloaded data folder as described in train/wan/README.md.
  4. Start training with the command given in train/wan/README.md and check the first logged steps.

Prompt for your agent

In the Yzmblog/DMAD repo, read train/wan/README.md, download the DMAD-Wan-data dataset to data/wan, and start the training command from that README. Expected output: training logs for the Wan2.1 DMAD student. Verify that the dataset folder contains the 42,158 teacher samples the README lists before starting training.

Caveat. The download command is inferred from the Hugging Face dataset link, so confirm the exact command in train/wan/README.md before running it.

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